What Spatial & Temporal GPU Optimization Is About
The Time Series Dynamic Programming Bin Packing (TS-DPBP) algorithm chooses where GPU work should run—across nodes, partitions, and clusters (spatial)—and plans when and how much to scale over upcoming intervals (temporal), so that the right capacity is in the right place at the right time. It combines time-series forecasting with application-behavior insights and multidimensional predictive analytics to prevent resource waste and bottlenecks, thereby enhancing GPU performance and enabling more sustainable, cost-effective AI deployments.
How Spatial & Temporal GPU Optimization Works
Adaptive prediction
A time-series module forecasts each workload’s demand for the next planning horizon (by interval). As new telemetry arrives, it refreshes the forecast so drift and pattern changes are reflected in the next plan.
Predictions drive execution
A dynamic-programming (DP) planner treats the horizon as a sequence of decision points and compares alternative placement-and-scaling paths. It minimizes a total cost that combines operational/utilization cost, SLA penalties, and transition costs (e.g., migration, resize, re-partition), so moves happen only when they clearly pay off.
Ready for large-scale
Heuristics (first/best fit packing rules, threshold rules, forecast-based fine-tuning) speed up planning to near-optimal results with light compute. The system favors stable placements and scales by evaluating the DP in parallel using wavefront (anti-diagonal) order.
Figure: The spatial &temporal algorithm to predict and optimize GPU usage